Development of a Portable Upper Limb Muscle Dynamometer for Assessment of Neuromuscular Injury Patients
Bibliographic record
Abstract
Introduction Patient recovery from neuromuscular injuries that cause upper limb dysfunction is commonly assessed via manual methods. Manual muscle testing is subjective, time consuming and requires extensive training. Existing dynamometers are more objective, but they are prohibitively expensive and impractically large, making them inaccessible to most clinics and patients with disabilities. Our aim is to develop a table-top upper limb muscle dynamometer that provides standard positioning, ease of use and portability while giving clinicians consistent and reliable quantitative data on a patient’s isotonic and isometric muscle power and strength, respectively. Methods The device consists of a lever arm, a brushless DC motor, a load sensor and an ergonomic cuff. It outputs analog data via standard BNC connectors. The device can be intuitively controlled by the operator to test various upper limb joints and motions. Isometric measurement repeatability was assessed by recording the maximal voluntary contractions of 18 healthy participants over three trials. Results The repeatability across 3 trials was 2.70±2.27 Nm (95th percentile: 6.74 Nm) for elbow flexion, and 2.83±2.13 Nm (95th percentile: 5.65 Nm) for elbow extension. Conclusions The dynamometer demonstrates a marked improvement in repeatability relative to manual muscle testing. Its small footprint and low cost can make it an easily accessible, standardized testing tool that requires little training to use. Future research and development will focus on using field-oriented control to measure isotonic muscle power in addition to isometric strength.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".